Single-cell transcriptomics is an unparalleled technique for obtaining a holistic understanding of biological systems, including tissue composition, organ development, and cellular differentiation. Many computational packages offer default end-to-end analysis for single-cell RNA sequencing (scRNAseq), performing common tasks such as dimensionality reduction, clustering, and differential expression testing. Furthermore, when processing multiple samples, there are a plethora of tools available for integrating datasets. While much attention has been given to the importance of batch correction, fewer studies examine biological covariates within datasets that can mask important signals and decrease statistical power. We examined the how strong biological signals can obscure interesting biology by examining the adult muscle precursor cells (AMPs) of the Drosophila wing-imaginal disc, a population of cells that develop into all adult flight muscles but exhibit high transcriptional homogeneity during larval development. We show that by using scRNAseq analysis tools with default settings, cell clusters are primarily split by two biological features: cell-cycle status and the biological sex of the donor fly. This stratification not only hinders the classification of AMPs into their canonical cell types, the “direct” and “indirect” AMP subpopulations, but also obscures the discovery of new biological signals and interactions by separating otherwise transcriptionally-similar cells. Cell-cycle and cell-sex signatures are observable within the latent space generated by dimensionality reduction, enabling post hoc correction by identifying the latent dimensions that correlate with markers of proliferation and biological sex. By removing these confounded latent dimensions from downstream processing, cell-type classification of AMPs is improved and the Hedgehog pathway is unmasked as an important signaling pathway for the proper development of adult flight muscles. With our analysis, we identified and validated two novel Hedgehog-signaling targets, Neurotactin and midline, within the AMPs. Additionally, confounding covariates can be supplied to the deep-learning single-cell software scvi-tools, taking advantage of its powerful integration models while suppressing unwanted biological signals. Our research highlights the importance of identifying and correcting for unnecessary biological signals in addition to technical effects, and provides easily-implemented solutions for removing signals such as cell cycle and cell sex.